Graph mining : laws, tools, and case studies
- Responsibility
- D. Chakrabarti, C. Faloutsos.
- Imprint
- Cham, Switzerland : Springer, ©2012.
- Physical description
- 1 online resource (xv, 191 pages) : illustrations
- Series
- Synthesis lectures on data mining and knowledge discovery ; #6.
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Chakrabarti, Deepayan.
- Contributor
- Faloutsos, Christos.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 167-190).
- Contents
-
- Introduction Patterns in Static Graphs Patterns in Evolving Graphs Patterns in Weighted Graphs Discussion: The Structure of Specific Graphs Discussion: Power Laws and Deviations Summary of Patterns Graph Generators Preferential Attachment and Variants Incorporating Geographical Information The RMat Graph Generation by Kronecker Multiplication Summary and Practitioner's Guide SVD, Random Walks, and Tensors Tensors Community Detection Influence/Virus Propagation and Immunization Case Studies Social Networks Other Related Work Conclusions.
- (source: Nielsen Book Data)
- Publisher's summary
-
What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints.
(source: Nielsen Book Data)
Subjects
- Subjects
- Social networks > Mathematical models.
- Computer networks > Mathematical models.
- Graph theory.
- Data mining.
- Data Mining
- Réseaux sociaux > Modèles mathématiques.
- Réseaux d'ordinateurs > Modèles mathématiques.
- Exploration de données (Informatique)
- COMPUTERS > Enterprise Applications > Business Intelligence Tools.
- COMPUTERS > Intelligence (AI) & Semantics.
- data mining
- social networks
- power laws
- graph generators
- pagerank
- singular value decomposition
Bibliographic information
- Publication date
- 2012
- Series
- Synthesis lectures on data mining and knowledge discovery, 2151-0075 ; #6
- ISBN
- 9781608451166 (electronic bk.)
- 160845116X (electronic bk.)
- 9781608451159 (pbk.)
- 9783031019036 (electronic bk.)
- 3031019032 (electronic bk.)
- DOI
- 10.2200/S00449ED1V01Y201209DMK006
- 10.1007/978-3-031-01903-6
